| Texto completo | |
| Autor(es): |
Rocha, Carlos
;
Pacheco, Lucas
;
Bastos, Lucas
;
Bittencourt, Luiz
;
Villas, Lenandro
;
Rosario, Denis
;
Cerqueira, Eduardo
Número total de Autores: 7
|
| Tipo de documento: | Artigo Científico |
| Fonte: | 2024 IEEE CONFERENCE ON NETWORK FUNCTION VIRTUALIZATION AND SOFTWARE DEFINED NETWORKS, NFV-SDN 2024; v. N/A, p. 6-pg., 2024-01-01. |
| Resumo | |
The deployment of 5G (and the upcoming 6G) networks has generated a significant demand for high-throughput and low-latency applications and services. While current wireless systems have integrated Mobile Edge Computing (MEC) capabilities, they still face challenges in efficiently delivering such services to mobile users in dense and dynamic scenarios. This paper introduces COOP, an algorithm to optimize the deployment of Unmanned Aerial Vehicles(UAV) to assist ground networks and provide MEC services for mobile users when static MEC services are not available. Driven by the need to improve service quality and reduce latency in densely populated urban areas, COOP employs a Genetic Algorithm (GA) to optimize UAV placement and service allocation for mobile users on the ground. This approach effectively manages dynamic network topologies and resource allocation challenges. Our results demonstrate that COOP outperforms existing approaches, showing up to 48.9% and 61.5% improvements in service rates compared to baseline algorithms in scenarios with 30, 50, and 100 mobile users. (AU) | |
| Processo FAPESP: | 23/00673-7 - Inteligência distribuída em redes de comunicação e internet das coisas |
| Beneficiário: | Nelson Luis Saldanha da Fonseca |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |